ArticleEnvironmental science & technology2021
Machine Learning Models of Arsenic in Private Wells Throughout the Conterminous United States As a Tool for Exposure Assessment in Human Health Studies.
Article in Environmental science & technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 103 citations in OpenAlex.
- Associations between a mixture of trace elements and bladder cancer occurrence: A study of possible reverse causation.Environmental epidemiology (Philadelphia, Pa.) · 2026Article
- Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures.Allergy · 2026Review
- Drinking water nitrate, disinfection byproducts, and prostate cancer incidence in the Agricultural Health Study.Journal of the National Cancer Institute · 2026Article
- Drinking water arsenic, urinary arsenic biomarkers, and cognitive impairment in the REGARDS study.Environmental research · 2026Article
- Surface Variable-Based Machine Learning for Scalable Arsenic Prediction in Undersampled Areas.GeoHealth · 2026Article
- Artificial Intelligence in Environment and Human Health: Progress, Opportunities and Challenges.Current environmental health reports · 2025Review
- Mild Drought Promotes Biomass Accumulation and Increases Diosgenin Content in Rhizomes ofPlants (Basel, Switzerland) · 2025Article
- Sociodemographic Disparities in Exposures to Inorganic Contaminants in United States Public Water Systems.Environmental health perspectives · 2025Article
- Cancer Risk and Estimated Lithium Exposure in Drinking Groundwater in the US.JAMA network open · 2025Article
- Article
- The private well water climate impact index: Characterization of community-level climate-related hazards and vulnerability in the continental United States.The Science of the total environment · 2024Article
- Multiple Data Imputation Methods Advance Risk Analysis and Treatability of Co-occurring Inorganic Chemicals in Groundwater.Environmental science & technology · 2024Article
- Long-Term Exposure to Arsenic in Community Water Supplies and Risk of Cardiovascular Disease among Women in the California Teachers Study.Environmental health perspectives · 2024Article
- Association of Water Arsenic With Incident Diabetes in U.S. Adults: The Multi-Ethnic Study of Atherosclerosis and the Strong Heart Study.Diabetes care · 2024Article
- Prioritizing water availability study settings to address geogenic contaminants and related societal factors.Environmental monitoring and assessment · 2024Article
- Estimating Lithium Concentrations in Groundwater Used as Drinking Water for the Conterminous United States.Environmental science & technology · 2024Article
- Defining high confidence targets of differential CpG methylation in response to in utero arsenic exposure and implications for cancer risk.Toxicology and applied pharmacology · 2024Article
- Impact of lowering the US maximum contaminant level on arsenic exposure: Differences by race, region, and water arsenic in NHANES 2003-2014.Environmental pollution (Barking, Essex : 1987) · 2023Article
- Cross-sectional associations between drinking water arsenic and urinary inorganic arsenic in the United States: NHANES 2003-2014.Environmental research · 2023Article
- Juxtaposition of intensive agriculture, vulnerable aquifers, and mixed chemical/microbial exposures in private-well tapwater in northeast Iowa.The Science of the total environment · 2023Article
Corrections and comments
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Authors and funding
12 authors at 7 institutions in 1 country.
Funding
Abstract
Arsenic from geologic sources is widespread in groundwater within the United States (U.S.). In several areas, groundwater arsenic concentrations exceed the U.S. Environmental Protection Agency maximum contaminant level of 10 μg per liter (μg/L). However, this standard applies only to public-supply drinking water and not to private-supply, which is not federally regulated and is rarely monitored. As a result, arsenic exposure from private wells is a potentially substantial, but largely hidden, public health concern. Machine learning models using boosted regression trees (BRT) and random forest classification (RFC) techniques were developed to estimate probabilities and concentration ranges of arsenic in private wells throughout the conterminous U.S. Three BRT models were fit separately to estimate the probability of private well arsenic concentrations exceeding 1, 5, or 10 μg/L whereas the RFC model estimates the most probable category (≤5, >5 to ≤10, or >10 μg/L). Overall, the models perform best at identifying areas with low concentrations of arsenic in private wells. The BRT 10 μg/L model estimates for testing data have an overall accuracy of 91.2%, sensitivity of 33.9%, and specificity of 98.2%. Influential variables identified across all models included average annual precipitation and soil geochemistry. Models were developed in collaboration with public health experts to support U.S.-based studies focused on health effects from arsenic exposure.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.